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Question 106/112
You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?
Correct Answer: A
TFRecord is a binary file format that stores your data as a sequence of binary strings1. TFRecord files are efficient, scalable, and easy to process1. Sharding is a technique that splits a large file into smaller files, which can improve parallelism and performance2. Dataflow is a service that allows you to create and run data processing pipelines on Google Cloud3. Dataflow can create sharded TFRecord files from your images in a Cloud Storage directory4.
tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.
Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.
References:
* TFRecord and tf.Example | TensorFlow Core
* Sharding | TensorFlow Core
* Dataflow | Google Cloud
* Creating sharded TFRecord files | Google Cloud
* [tf.data.TFRecordDataset | TensorFlow Core v2.6.0]
* [tf.data: Build TensorFlow input pipelines | TensorFlow Core]
* [Vertex AI Training | Google Cloud]
* [NVIDIA Tesla V100 GPU | NVIDIA]
tf.data.TFRecordDataset is a class that allows you to read and parse TFRecord files in TensorFlow. You can use this class to create a tf.data.Dataset object that represents your input data for training. tf.data.Dataset is a high-level API that provides various methods to transform, batch, shuffle, and prefetch your data.
Vertex AI Training is a service that allows you to train your custom models on Google Cloud using various hardware accelerators, such as GPUs. Vertex AI Training supports TensorFlow models and can read data from Cloud Storage. You can use Vertex AI Training to train your image classification model by using a V100 GPU, which is a powerful and fast GPU for deep learning.
References:
* TFRecord and tf.Example | TensorFlow Core
* Sharding | TensorFlow Core
* Dataflow | Google Cloud
* Creating sharded TFRecord files | Google Cloud
* [tf.data.TFRecordDataset | TensorFlow Core v2.6.0]
* [tf.data: Build TensorFlow input pipelines | TensorFlow Core]
* [Vertex AI Training | Google Cloud]
* [NVIDIA Tesla V100 GPU | NVIDIA]
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